Readit Newshttps://notebooks.azure.com/wesm/libraries/python-for-data-a...
If you want to read, click on any notebook, for example:
https://notebooks.azure.com/wesm/libraries/python-for-data-a...
If you want to run, click Clone, sign in, then Run. It's basically a collection of Jupyter notebooks. This is from his personal repo.
[disclaimer: work at msft]
https://medium.com/dunder-data/python-for-data-analysis-a-cr...
That said, it's obviously an alpha release, so who knows what the future will hold.
"if a computer could - like humans - learn to produce infinite, novel, contextual, and meaningful grammatical utterances"
To perfectly achieve this goal, you might have to simulate 4 billion years of evolution under the same conditions as it happened on Earth, and a few thousand years of cultural evolution as it led to our languages and our cultural context. Language is incredibly complex and changing, many of its details might be incidental, i.e. results of random events, so it seems unreasonable to pretend that we can deduce it all from some elegant first principles. At least that is my reading of Norvig's argument.
If that is the case, then the argument that Norvig is making is irrelevant to the argument Chomsky is making. Chomsky simply makes the point that statistical accounts lack explanatory adequacy. As someone who has worked closely with many of his students and who has received extensive training on his scientific program, I can say with confidence that Chomsky would have no objection whatsoever about the usefulness of statistical approaches to linguistic engineering problems. The results speak for themselves. He would go on to say, however, that how well a statistical approach solves a linguistic engineering problem is irrelevant to the question of how humans do what they do.
The answer to the question may well be statistically grounded. That is a valid hypothesis and a logical possibility which should be taken seriously. However, it is incumbent on the proponents of such an answer to provide evidence that it is what humans are doing. Here are some examples of the kinds of evidence necessary:
* evidence that humans are capable of performing the kinds of computations that the statistical approach requires,
* evidence that the statistical approach works with the relatively limited amount of data that a human receives,
* evidence that the statistical approach fails in ways that humans fail
How well a statistical approach succeeds at an engineering task is not an item on this list, simply, again, because engineering tasks are irrelevant to what humans actually do.
Let me specifically say that statistical approaches are not, from the start, ruled out as potential candidates for the algorithms underlying human language. It's just that a case has to be made for them using the right kind of evidence.
Finally, I'll reiterate what others have pointed out: from a scientific perspective, that something is hard to explain doesn't mean that we shouldn't try. And, those that have given up (as you suggest Norvig has) shouldn't fault those who haven't for calling them out on it.